{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c2809ed7-ede4-4008-8b85-4605b49e883e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from pylab import *\n",
    "from matplotlib.font_manager import FontProperties  \n",
    "import matplotlib.pyplot as plt  \n",
    "#支持中文\n",
    "mpl.rcParams['font.sans-serif'] = ['SimSun']\n",
    "plt.rcParams['axes.unicode_minus']=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4735b2cd-6f2e-4289-aeab-65aa3e76d34c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['25cm风速.xlsx',\n",
       " '30cm风速.xlsx',\n",
       " '40cm风速.xlsx',\n",
       " '50cm风速.xlsx',\n",
       " '60cm风速.xlsx',\n",
       " '70cm风速.xlsx',\n",
       " '80cm风速.xlsx',\n",
       " '90cm风速.xlsx',\n",
       " '100cm风速.xlsx']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list0 = os.listdir()\n",
    "list1 = list0[1:-3]\n",
    "list1.sort(key=lambda x:int(x[:-9]))\n",
    "list1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "45606a17-7e7e-4b30-8b6b-77609b9f4fce",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_cal1(list1):\n",
    "    mean1 = []\n",
    "    for i in list1:\n",
    "        dat = pd.read_excel(i,header = 0)\n",
    "        dat.columns = np.arange(4)\n",
    "        mean1.append(dat[1].mean())\n",
    "    return mean1\n",
    "def mean_cal2(list1):\n",
    "    mean2 = []\n",
    "    for i in list1:\n",
    "        dat = pd.read_excel(i,header = 0)\n",
    "        dat.columns = np.arange(4)\n",
    "        mean2.append(dat[3].mean())\n",
    "    return mean2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2ba705da-e196-4ca8-8875-5a2b11d61c04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5.26077412925,\n",
       " 5.4956240035,\n",
       " 5.720651832000001,\n",
       " 5.895184457250001,\n",
       " 6.156943771500001,\n",
       " 6.453054957250001,\n",
       " 6.52337859575,\n",
       " 6.673751314499999,\n",
       " 6.67083078775]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean1 = mean_cal1(list1)\n",
    "mean1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e174197d-cd3b-412f-86ac-a6e11252ae5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5.34771175125,\n",
       " 5.5366356,\n",
       " 5.7329385145,\n",
       " 5.981945689,\n",
       " 6.192481201499999,\n",
       " 6.404512016000001,\n",
       " 6.4537306915,\n",
       " 6.6166583075,\n",
       " 6.618308494999999]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean2 = mean_cal2(list1)\n",
    "mean2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "27a94685-4089-478e-8424-a10b8eabedf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    19999.500000\n",
       "1        5.260774\n",
       "2    19999.500000\n",
       "3        5.347712\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat1 = pd.read_excel('25cm风速.xlsx',header = 0)\n",
    "dat1.columns = np.arange(4)\n",
    "dat1\n",
    "dat1.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8fbfcbba-af2d-49ed-a43d-262a5edef7b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    19999.500000\n",
       "1        5.495624\n",
       "2    19999.500000\n",
       "3        5.536636\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat2 = pd.read_excel('30cm风速.xlsx',header = 0)\n",
    "dat2.columns = np.arange(4)\n",
    "dat2.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cb1fbaa9-4280-4225-ad86-af1ef1c4b168",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.8544457 , 0.8925896 , 0.92913823, 0.95748551, 1.        ,\n",
       "       1.04809386, 1.0595157 , 1.08393897, 1.08346463])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean1/mean1[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d4e06a9d-97d7-498c-b70e-023a754ff4d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.25\n",
       "1    0.30\n",
       "2    0.40\n",
       "3    0.50\n",
       "4    0.60\n",
       "5    0.70\n",
       "6    0.80\n",
       "7    0.90\n",
       "8    1.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1 = [0.25,0.30,0.40,0.50,0.60,0.70,0.80,0.90,1.00]\n",
    "X1 = pd.Series(x1)\n",
    "S1 = pd.Series(mean1)\n",
    "S2 = pd.Series(mean2)\n",
    "X1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f9f14906-9d90-4b5c-898a-2bec8a60e19d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 2.0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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161yfT5o0iSeeeAKAioqKWnM3DoeDsLCwOl9n+fLlLFu2rNb6tLQ0IiLq+3vK/6Smpvq6BI8JpLZA22hP+LlzTLhqXX3zLXWJjz3LA099SXT0RT74oKUVNl6gvDdlVWfleVPAhEt+fj5du3YFnHM11YESGxvLsWPH3PYtLy+nS5cudb7O0qVLWbhwoWu5pKSE3r17M27cuFo9IH9kt9tJTU1l/PjxhISE+LqcFgmktkAba09+Psyd67aqKeHy0b+6Eh43xCN1NkagvTcFBQVe/xoBES4HDx5kypQpZGdnA2AYBuHh4QAkJia6TeAfPnyY0aNH1/taoaGhhIaG1lofEhISEN9U1QKpPYHUFmgj7eneHQYOhOxs1+X3jQkXBxYuxgygU79Yn9wbJlDem9Zog6kn9O12u6sH4nA4mDlzJunp6bX2i4mJ4cYbb3Qtp6WlMWXKFABGjBhBREQER48eBWDr1q08+uijrVC9iNTLYoHHH3db1dieywcD5+umY37AlD2XrKws3nvvPc6cOcOiRYu4//77GTZsGHv27CEnJ8ft7C9whsvcuXNJSUkhODiY48ePk5yc7Nq+fv16kpOT6du3LzabjUWLFrVyi0Skljlz4Je/dF5A6XBwtmp1feFSiZVywkkpns3M1qpRms2U4TJw4ECWLFnCkiVL3NZXD3vVJSkpiaSkpDq39enThzVr1ni0RhFpoc6dYcMG5xX6Viu5Vde71HW2mGGxgmFhOhvZe6Qzly9DPefkiEmYelhMRALcxInw/vsQHl7nsJgDC4bFgiUiHPuWD+j14ARGjIDLl31RrDSFKXsuItKGTJzI5aNHKerhjJWa4ZLNAIL+z3z6J88hrFMn1k5zXtDfhJt3iI8oXETE585WdUVCgaihQ6nY/AE9h0RSQBfOLrFAp+/2VbD4B4WLiPhcbq5zUKw7YBk6lJPWfhQAERHQrZtPS5Nm0t8AIuJzZ886zxXrATBoENXn7vTvr7OO/ZXCRUR8rmbPhfh4t3AR/6RwERGfqw6XHgDx8VTfsWnAAJ+VJC2kcBERn8utejBLdbio5+L/FC4i4nO5VV2VHsHB0LOnei4BQOEiIj6Xe+oUAD169gSr1dVzUbj4L4WLiPjc2fPnAegxYADFxVBU5Fzfr5/PSpIWUriIiE9VVlZy7uJFALpfd51rSKxbN+jQwYeFSYsoXETEp86fP4/DMLAA3UaMcIWLJvP9m8JFRHyq+jTkbkDwkCGabwkQChcR8ancnBxApyEHGoWLiPhU7sGDAPSwWCAuTqchBwiFi4j4VO433wDQo2NHCApSzyVAKFxExKdyq9KkR7duOBxw/LhzvXou/k3hIiI+dbrq1i+9evfmzBmoqIDgYIiL83Fh0iIKFxHxqdMFBQD0GjTINd/Sp48zYMR/KVxExKdOl5YC0HP4cJ2GHEAULiLiM1cqKjh35QoAvW68URdQBhCFi4j4zLnMTBxAENDthhvUcwkgChcR8ZnTe/cCzlvtB4WG6jTkAKJwERHfMAxXuPSKiADD0AWUAUThIiKtq7gYVq6EQYM4/ec/A9CzpISyuEH85MxKOlGscAkAChcRaT07dzovYFmwALKzOVO1uhcQdiab37OAHOLosnenL6sUD1C4iEjr2LkTJk+G8nIwDOewWNWmXoAVAysG4ZTDlMnO/cVvKVxExPuKi2HGDGeoOByu1TXDpVoQDgyH4dy/uLgVixRPUriIiPe98QaUlbkFC9QdLgBWw4FRVsap5/9CRgZkZMDp04gfUbiIiHcZBrz8cp2bqudcetZzmO3FFBISDBIS4L77vFaheIHCRUS8q6AAsrKcaVFDKVBS9fnVPRdwzsHEk0U0hYSFwU9/6u1CxZN0azgR8a6qe4ddrXqUK7LqX31GDbjIy9uiuf56Txcm3qRwERHv6tChztX1zbdcbdunkUT09mhF0go0LCYi3hUdDQMHgsXitrqh+RYABxYuxAwkIq6LV8sT71C4iIh3WSzw+OO1Vjem5/LBwPm1Qkn8g8JFRLxvzhyIiADrd79yGgqXSqyUEUFK8exWKU88T+EiIt7XuTNs2ODshVQFTH3hYlisgIXpbGTvkc5cvtyKdYrHKFxEpHVMnAjvvw/h4WCxuN1XDJxzLA4sWCLCsW/5gF4PTmDECBQufkrhIiKtZ+JEyMmBl15y9VyqJ/SzGcCrQ16C06cJmzaBtWshPd3Z6RH/o3ARkdbVuTOO//xPcqsWe/33f/P6b/IZxBE+HTkfOnVy7WrVbyi/pbdORFpdXl4eV3D+AuqekMBZezRgqZkr4ucULiLS6k5X3YUyFgju1IkLF5zrNQQWOBQuItLqzpxxTuf3BGjf3nVnfYVL4FC4iEiryzl+HIA4cAsXDYsFDoWLiLS6U8eOAdAboH17DYsFIIWLiLS6UydOANDbaoV27TQsFoBMGS7Z2dm8+uqr9O3bt9HHfPLJJ6SkpJCSksIjjzzC8apuN8CsWbOIi4tz/Zs+fboXqhaRxjp16hQAvUNDARQuAciUt9wfMGAA8+bN49e//nWj9r9w4QKvvfYab7/9NgD79u1j0aJFbNiwAYBbbrmFdevWea1eEWmaU1UT+r0jIgBcw2Kacwkcpuy5VLM28gqq7Oxs/vWvf7mWhw8fzsGDB13LwcGmzFCRNsnhcJCTlwdA78hIDEM9l0AUEL91R44cyZtvvulaPnToEMOHD3ctX7p0iYULF1JWVsb58+dZtWoV3bp180WpIm3e+fPnqbDbsQA9O3bk8mWoqHBuU7gEjoAIl6CgIG699VbXckpKCk8//bRrOSMjg9WrVxMWFsb27dtZsmQJa9asqfO1bDYbNpvNtVxS4nzKt91ux263e6kFrae6DWqL+bSV9hyrOlOsBxDUvj255+1ACFarQWjoFczY/EB9b7zJYhiG4fWv0kz9+vVzm5hvjPXr1xMdHc2dd95Z53aHw8HgwYM5evRonduTk5NZtmxZrfVvvfUWEVXjwyLSfLt37+Y3v/kNicC2UaN479Hf8p//mUSHDhWsX7/D1+W1CWVlZdx3331cuHCBjh07euVrBETPpdqHH35Ip06dagVLfn4+Xbt2BZzzOA3l6dKlS1m4cKFruaSkhN69ezNu3Diio6O9U3grstvtpKamMn78eEJCQnxdTosEUlug7bQnOzsbcF7j0rVvX0aNug2AmJgQJk2a5ItSrynQ3puCggKvf42ACZf09HTOnTvHnDlzADhw4ADDhg3j4MGDTJkyxfUNbRgG4eHh9b5OaGgooVWnR9YUEhISEN9U1QKpPYHUFgj89lTf+qU3YI2MpLTU+WuoUyeL6dsdKO9Na7TB1GeL2e12Vy/D4XAwc+ZM0tPTa+1XXFzMjh07XMFiGIbr1OOYmBhuvPFG175paWlMmTKlFaoXkbq4rnEBXZ0fwEzZc8nKyuK9997jzJkzLFq0iPvvv59hw4axZ88ecnJySEhIcNv/zTffJCUlhVdffRWA8vJyxowZAzjDZe7cuaSkpBAcHMzx48dJTk5u7SaJSJWcnBzgu3DRaciByZThMnDgQJYsWcKSJUvc1lcPbV3tscce47HHHqv39ZKSkkhKSvJojSLSPFf3XBQugcnUw2IiElgqKytdz3K5elhMV+cHFoWLiLSas2fPUllZSbDFQndQzyWAKVxEpNVUD4n1DA0lCBQuAUzhIiKtxjXf0q6dc4WGxQKWwkVEWo0rXKpvJqueS8BSuIhIq3GFS/VdMux2ioucnytcAovCRURaR3Exp9LSAOhdVORcN306278dxHxWknuo2He1iccpXETE+3buhLg4TmVmAlWnIVfp68jm9yzg7vlxzv0kIChcRMSrLB99BJMnQ3k5p6rW1QwXKwZWDNpVlmNMnqyACRAKFxHxmuDSUoLuuQcMgwqHg7NV63vXsW8QDgyHATNmfPdoSvFbChcR8Zo+aWlQVgYOB7mAAbQDutazv9VwYJSVcer5v5CRARkZUHVBv/gZhYuIeIdh0P/9912L1UNicTT8i8cwwPZiCgkJBgkJcN993ixSvEXhIiLeUVBAh7NnsVSddlzXfEtdrBjEk0U0hYSFwU9/6tUqxUtMeVdkEQkApaVuiyeqPvZp5OGjBlzk5W3RXH+9R6uSVqJwERHv6NDBbbE6XPo18vBtn0YSca1ujpiWhsVExDuioynt3h3DYgG+C5e+1zjMgYULMQOJiOvi1fLEuxQuIuIdFgvHJk92LR6v+nitcAH4YOB8qAol8U8KFxHxmpPjxkFEBIbF0qhhsUqslBFBSvHsVqhOvEnhIiJec6VDByrfeYcCi4WyqnX1TaMYFitgYTob2XukM5cvt1KR4hUKFxHxKmPCBI6/9BIAPYDQq7dbLGCxYIkIx77lA3o9OIERI1C4+DmdLSYiXneiZ08A+kZGwsWL7hsHDID582HOHMI6dWLtNHA4wKo/ff2a3j4R8boTJ5wzLv3CwwHYkpRCP47x+Mx8LEeOOMOlxqMoFSz+Tz0XEfG648ePA9C3pASAv+Z+nxP0Y+wkQCeFBST9fSAiXlfdc+l7+TJGUBAbDjkvux83zpdViTcpXETE61zDYsDFXtdRboQxZAj06uXTssSLFC4i4nWungtwOHwUALff7sOCxOsULiLiVRcuXKC46uFffYF/FCtc2gKFi4h4VXWvJdpqpT2w85wzXH74Q9/VJN6ncBERrzp58iQA/RwOADIZxejR0LW+x1FKQFC4iIhX1ZxvKQnvxjm6a0isDVC4iIhXVfdc+gKZjAY039IWKFxExKtqnob8r/JRBAXBv/2bT0uSVqBwERGvqjkslskobroJOnb0bU3ifQoXEfEq92GxURoSayMULiLiNTabjfPnzwPQnRC+ZYjCpY1QuIiI1+Tl5QHQETjNMKztQvje93xbk7SOJoVLZWWlt+oQkQBUHS59gS8Zzfe+B1V33ZcA16Rb7kdGRjJixAhuuOEGEhISGDNmDCNHjiQkJMRb9YmIHzt37hwAA9F8S1vTpHAJCwtj79697N27F4vF+RCG4OBghg0bxpgxY1z/Ro8eTVhYmFcKFhH/cfbsWQAGABmM4r8ULm1Gk8KlsLCQo0eP8sUXX7B3716++OILMjIy2L9/P/v372ft2rUABAUFMWTIEMaMGUNCQgLz58/3SvEiYm55Z84AznB5N8J5GrK0DU1+EmV8fDzx8fHcd999AMyaNYu33nqLN954g/3797Nv3z4yMzM5cOAABw4cYP369QoXkTbqfE4OAJFEM/wHXWjXzscFSavx2GOOH3jgAR544AHX8smTJ11hIyJtj2EY5BYUAFDGcJKSfFyQtCqPhcvV+vTpQ58+fZg2bZq3voSImFh+fj5ldjsAZxnLNM23tCm6zkVEPM8wOFY1atELOBkxhlGjfFuStC6Fi4h4TnExrFwJgwZxasoUwDmZ/4LxC4JeWencLm1Ck8LlTNWZHyIitezcCXFxsGABZGeTXbV6ABBdfsq5Pi7OuZ8EvCbNucTFxdGrVy8SExNJTExk7NixlJWVeas2EfEXO3fC5MlgGM5/4BYuVgwwgPJy537vvw8TJ/qqWmkFTQqX4cOH880337Bp0yY2bdrkupAS4N5772XUqFGMHj2a0aNH06NHj2YXlZ2dzUcffcTy5ctdt+u+lhMnTvDUU0/Rv39/rly5wvPPP++qr6FtItJCxcUwY4YzVKoeZQzu4eLicIDV6tw/Jwc6d269OqVVNWlY7Msvv+TixYvs3r2b//7v/+bhhx9m9OjRBAcH8+677/LLX/6SKVOmEBcXR2xsLBMnTmTJkiVNLmrAgAHMmzcPo+ovoMZ4+OGHefLJJ3n22WeJiYnh9ddfb9Q2EWmZ4pVvYJSVuQUL1BMuAA4HRlkZxSl/aY3yxEeaPKEfGhpKYmIi8+bNY/Xq1aSnp1NaWsrevXv54x//yKOPPkpCQgIlJSWkpqby//7f/2t+cdbGlZeVlUVRURFDhw4FYOrUqaxateqa20SkhQyD0t+8zNV/B1YAp6o+rxUuzsO4tDyFWgdKwGjSsNjatWuZMmUKMTExbutDQkJISEggISGBRx99FIArV65w4MAB0tPTPVdtPXbv3k1CQoJrOT4+nsOHD2Oz2RrcFhoaWuu1bDYbNpvNtVxSUgKA3W7HXnXOvj+rboPaYj5+2Z78fOIuZ9VafRJwAOFAbB2HWTHodTkL+7lzEB3t5SJbzi/fmwa0RjuaFC4//elPCQoK4uabb+auu+5i2rRpDBo0qO4XDg5m1KhRjGqFk9tzc3OJvuobNDIykoKCgga39ezZs9ZrLV++nGXLltVan5aWRkREhGcL96HU1FRfl+AxgdQW8K/2hJ87x4Q61lcPifUHGprdTNu6lfLYuuLHnPzpvWlIa5yI1aRw+fzzz9myZQtbt25l8eLF/OIXv2Dw4MHcfffdTJs2jZtvvtlbdTaooqKi1vyMw+EgLCyswW11Wbp0KQsXLnQtl5SU0Lt3b8aNG1crpPyR3W4nNTWV8ePH+/2jEgKpLeCn7cnPh7lza62ud77lKuOmTfObnovfvTcNKKi6LY83NSlcbr75Zm6++WaWL19OVlYWmzZtYuvWraxYsYIXXniBbt26MW3aNH784x+TlJRU57CTN8TGxnLs2DG3deXl5XTp0qXBbXUJDQ2ts+6QkJCA+KaqFkjtCaS2gJ+1p3t3GDgQsrPd5k+uGS4WCwwYQEhsrPNzP+FX700DWqMNzb5Cf+DAgfz85z/nH//4B+fOneP1119n7NixvPnmm0ydOpWuXbsyY8YM1q1bR2FhoSdrriUxMdHtBpmHDx9m9OjR19wmIi1kscDjj9da3aiey/z5fhUs0jQeuf1LdHQ0Dz30EJs3byY/P59Nmzbxv/7X/2LXrl3MmTOH2NhYfvjDHzb5de12u2tIy+FwMHPmzDpPEBgxYgQREREcPXoUgK1bt7pOLGhom4h4wJw5EBHhvH6lSoPhYrU69589uzWqEx9pUrg88cQT/O1vf2twn7CwMKZNm8af/vQncnNz+ec//8mCBQtcT6RrjKysLH7zm99w5swZFi1aRHp6OhUVFezZs4ecqudDXG39+vX8+te/ZtmyZRQWFrrd/r+hbSLSQp07w4YNGBYLlVW/UqoHoq8Ol0qsGBYLbNyoCygDndEEFovFCAkJMd56662mHObXLly4YABGfn6+r0vxiIqKCmPz5s1GRUWFr0tpsUBqi2H4f3tK3vvQuGRtbxQ4b/RiAMalqhvCOCwWoxKLccna3ri4YaevS20yf39vrpafn28AxoULF7z2NZo8LHblyhVmz57NW2+9dc199+3bx2effdbkwBMR/xP5k4mEnc9hUzvns4y7A9Un71sGDMC68iXC8k/TYXpdJy9LoGlyuNx55520b9+eOXPm8Oabbza479atW7ntttuaXZyI+Jdzts4crXBehNyndx8++uMfsefmwpEjMH8+1qhOPq5QWkuTw2Xs2LHs3LmTiIgIHnzwQdatW+eNukTED33+OTiqpvMHXz/UeYFkdLTOCmuDmnW22NixY/noo49o3749Dz/8MH/5i25AJyLw5cd5nKMUgPgat12StqfZpyJfHTBvvPGGJ+sSET9U+OmXHKn6PP76631ai/hWi65zSUxMJDU1lcjISH7605/y5z//2UNliYi/KS+H8MOZrnCp776D0ja0+CLKm266yRUwjzzyCGvWrPFEXSLiZ9LTYYBjL+erlgcOHOjTesS3PHKF/o033sjHH39Mx44d+d//+3/zpz/9yRMvKyJ+ZNcu6EgGAN06daJjx44+rkh8qUk3rmxIQkKC666hc+fObdJTJEXE/33xWQW9qs4U05CYNPn2Lw190yQkJPDxxx/TqVMn5s2bx8aNG1tcoIiYn2FA/mffcJxKAAYNH+7jisTXmtRz+d3vfnfNfcaMGcPHH3/M+PHj+eqrr7Do/HaRgHfkCPQp1mS+fMcjcy5Xu+GGG/j444/rfWaKiASWXbtgJDVOQ46P92k94nteCReA0aNH89VXX/HOO+9460uIiEl8/jmMQj0X+Y7XwgWgR48e/OQnP/HmlxARE9i1C/qxj/yqZfVcxKvhIiKBr7AQCg6do6QqWrrHxhIZGenjqsTXFC4i0iK7d7sPicVrSExQuIhIC+3a5QyXo1XLmm8RULiISAtpMl/qonARkWaz22HPHvfTkBUuAgoXEWmBzEyoLLcxlEOuYTGdKSagcBGRFti1C4ZyiFKu6DRkcaNwEZFm+/xz55DYN1XLcXFxdOjQwac1iTkoXESk2aon87+tWr7uuut8Wo+Yh8JFRJrl5EnIyYHRZLp6LgoXqaZwEZFm+XyXQTTnuTEoQ+EitShcRKRpios59sRKxs4aRD7d6FRZ5AqXIQcOQHGxL6sTk1C4iEjj7dwJcXH0XbmAvpXOp07agayqzdetWgVxcc79pE3z2GOORSTA7dwJkydjGAZWvnuMeRZwBWgP9AIoL4fJk7Fs2eKbOsUU1HMRkWsrLoYZM8AwsDgcbptcZ4oBFgCHAwyDoHvuIbi0tHXrFNNQuIhIvU6fhowMOPVfb2CUlTmD4yquyfyaKx0OKCujz6eftkKVYkYKFxGp18yZkJBgYPvtyxhG3fvUGS5V+m/fTr0HSkBTuIhIvR55BHqFFhBPlts8S02uM8WuWm8xDDqcPet8mpi0OQoXEanX7NmQtq3+eRODhnsuAFy86OGqxB8oXESkQYNuqP9eYeeBYpwT+fXeaF+PPG6TFC4i0rDoaBg4EIfzXDA31b2W/kDYVdsMi4XS7t2hSxdvVygmpHARkYZZLPD443VEy7WHxI5NmeI8XtochYuIXNucOZRbIqi86ldGfZP5WK0QEcHJH/6wFYoTM1K4iMg1XQ7rzE8sGzCwuAVMnT0XqxUsFirffZcrerZLm6VwEZFrKi+HMyMm8vuk9ykn3HVScs2r8x1YMCwWCA+HDz7AGD/eR9WKGShcROSaoqKcV+pP/N1E4jjFJdpTDhyr2j4EsAwcgOWll5yX9U+Y4LtixRR040oRaRSrFb79FqIopgOX2BcUhFFZSXRUFN0OH8YSHa3Je3FRz0VEGu3bb2EsewA40KcPAMNGjMDStauCRdwoXESk0dzCJSoKgGHDhvmyJDEphYuINNrhw3AzuwE4UHVDSoWL1EXhIiKNYhiQ/U0FN7APgAP5+YDCReqmcBGRRsnLg34lmYRh41JUFMdycgCFi9RN4SIijVJzvuWboUMxDIOYmBhiYmJ8XJmYkSlPRS4qKmLBggX069ePwsJCVqxYQVjY1bfFc9erVy9sNptrP7vdzsmTJwkNDWXWrFmkpaW59k1MTGTjxo1ebYNIoPn22+/mW77u1g1Qr0XqZ8qey6JFi7j33ntJTk5m3LhxPPfccw3uf/HiRe655x7y8/PJycnh66+/5pe//CWhoaEA3HLLLeTk5Lj+KVhEms7tTLFg59+lChepj+nCpbS0lI8//pgJVVf43nHHHaxduxZHHc/urmaz2Vi8eLFrec2aNTz00EOu5eBgU3bQRPzKma8KGMRRAA5cuAAoXKR+pvutm5mZyZAhQ7BanbkXHh5Ox44dyc7OJj4+vs5junbt6vr84sWLlJSUEFnjAUWXLl1i4cKFlJWVcf78eVatWkW3qm791Ww2GzabzbVcUlICOIfZ7HZ7i9vna9VtUFvMx+ztCf/K2Wu5FDeYA4cPAzBkyJB66zV7e5oikNoCrdMO04VLbm4u0dHRbuuioqLIy8urN1xqWr16NXfffbfbuoyMDFavXk1YWBjbt29nyZIlrFmzps7jly9fzrJly2qtT0tLIyIiogktMbfU1FRfl+AxgdQWMGd77HYLfXP/B4AjPXtw4ou/A3D69Gk++OCDBo81Y3uaK1DaUlZW5vWvYbpwqaiowDAMt3UOh+OaE/rVtm3bxsKFC93WrVu3zvX5pEmTeOKJJ+o9funSpW7Hl5SU0Lt3b8aNG1cr9PyR3W4nNTWV8ePHExIS4utyWiSQ2gLmbs8330AH/huAsu8nwBd/p3v37tx77731HmPm9jRVILUFoKCgwOtfw3ThEhsbS2Fhodu64uJiunfvfs1j9+3bR6dOnWqtz8/Pdw2dWa3WWuFVU2hoqOtEgJpCQkIC4puqWiC1J5DaAuZsT3aWwb/xBQDfRjqf0TJs2LBG1WnG9jRXoLSlNdpgugn9MWPGcODAAVcAXLp0CYfDQY8ePa557GeffUZsbKzbuoMHD5KYmOhaNgyD8PBwzxYtEuDydh2hC0VUBIVx4OJFQJP50jDThUtUVBS3336767qUHTt2MHv2bAzDYObMmaSnp9d77JEjR6ioqHBbFxMTw4033uhaTktLY8qUKd4pXiRAWb5wTuaf7TmGrw4cAGD48OG+LElMznTDYgCvvPIKixcvZteuXeTl5fHiiy9SUVHBnj17yMnJISEhoc7junTpUqtXEhMTw9y5c0lJSSE4OJjjx4+TnJzcCq0QCRxdDjsvniwbcTOZ6W8CMGrUKF+WJCZnynDp3Lkzq1evrrU+Ozu7wePqC42kpCSSkpI8UZpImzQg39lzKRk9hHMfnMNisWhYTBpkumExETGXwtPlDLuSCcC5/s7T8QcNGkT79u19WZaYnMJFRBp0+v19hHCFPGssh4vPAjBy5EgfVyVmp3ARkQaVpznnW450uZnML78ENN8i16ZwEZEGhe13zrfkDxxLZqZzeEw9F7kWhYuI1GYYkJ8Px4/T48TnAJSPHsOhQ4cA9Vzk2hQuIvKd4mJYuRIGDYKYGOjfn5jyHAwg/OwO7HY7nTp1ok+fPr6uVEzOlKcii4gP7NwJM2ZAPTc1LNmyEoCRvXtjsVhaszLxQ+q5iIgzWCZPhvJy55DYVfffswBfVn0+8sAB5/4iDVC4iLR1xcXOHothQAMP5asOl1Hg3L+42Pu1id9SuIi0dW+8gVFW1mCwAGRWfRxpGBhlZZx6/i+cPu398sQ/KVxE2jLDgJdfvnoUrJZzVf8swPCqw2wvpnDfzGscKG2WwkWkLSsogKwsrDQcEtVDYvFAe8CKQTxZ/Me9hQ0cJW2ZwkWkLSstbdRu+6s+Xn11yz2TLnqyGgkgCheRtqxDh0btllH1cczVGyIjPVmNBBCFi0hbFh0NAwfCNa5bqX5En+tJShaL87guXbxZnfgxhYtIW2axwOOPN7jLBeBI1eduPZf5868ZStJ2KVxE2ro5cyAiAqx1/zrYX/WxD9AVqMTq3H/27FYqUPyRwkWkrevcGTZsAIsFo46AqTkkVokVAwsVb290HidSD4WLiMDEifD++xAejgOL24nJ1eEyBrAHh/P4gA8ou3WCD4oUf6JwERGniROx5OTASy/hCAl1rXb1XObOJSz/NH84MkGdFrkmhYuIfKdzZ6zzH+dK1Q3Tn7l1M4erJu0Tnn0WOnWqb2pGxI2+TUTE3cmThNovUUEIBcM6YhgGcXFxdOvWzdeViR9RuIiIuwMHAPiG6zCCnLerTEhIaOgIkVoULiLipmzv1wAcYBgFBc5r88eMqXVtvkiDFC4i4ubCLmfP5UzUcL76yjmdr56LNJXCRUTcWA46ey7lgwbyzTffAAoXaTqFi4h8p7KSLmcPAnBhgBWHw0GPHj3o3r27jwsTf6NwEZHvHDtGu8rLlBPGpU65gHot0jwKFxFxKfvCOSR2kOvJy98LwE033eTLksRPKVxExOVcmnMy/1jEcDIz9wAwduxYX5YkfkrhIiIuFenOnsu5uH4cPXoUgMTERF+WJH5K4SIiLuHHnD2XEz2DABg8eDBRUVG+LEn8lMJFRJzsdnpccJ56fLZjIaAhMWk+hYuIAFCWeYQQw85FOpBz8VtA4SLNp3AREQBO7XQOiX0bfD2ZmV8AChdpPoWLiABQsss5mf9ldB8KCwsJDQ1l5MiRPq5K/JXCRUQAsBxy9lwOxrQDnDerbNeunS9LEj+mcBERAKJznT2X4+GXAA2JScsoXESEy8WX6W1zXtdy9NIJQOEiLaNwERGyPviWYCo5Q2cOHnEOjylcpCUULiLCub85h8Q+6tAHu91Ojx496Nevn2+LEr+mcBERbBnO3sqeyDAAvv/972OxWHxZkvg5hYuI0P6Ys+fydbBzMv/WW2/1ZTkSAIJ9XYCI+JBhYD9bQL8L+3EAXxU4J/O///3v+7Yu8XvquYi0RcXFsHIlDBpESM8Y+nCKQ8CFslLat2vHaM23SAspXETamp07IS4OFiyA7GzX6s+qPo6tqCC4Xz/nfiLNpHARaUt27oTJk6G8HAzD+a/KrqqPt4Jz++TJChhpNlPOuRQVFbFgwQL69etHYWEhK1asICwsrMFjZs2aRVpamms5MTGRjRs3AnDixAmeeuop+vfvz5UrV3j++ed1Joy0PcXFMGOGM1Acjlqbq3su3wfndqvVuX9ODnTu3Hp1SkAwZc9l0aJF3HvvvSQnJzNu3Diee+65ax5zyy23kJOT4/pXHSwADz/8ME8++STPPvssMTExvP76694sX8SUile+gVFWVmewnAGO4fyFcHP1SocDo6yM4pS/tF6REjBMFy6lpaV8/PHHTJgwAYA77riDtWvX4qjjB6Km4OC6O2FZWVkUFRUxdOhQAKZOncqqVas8W7SI2RkGpb95ueYomJvqIbGRQEf3w7i0PIV6DxSph+mGxTIzMxkyZAhWqzP3wsPD6dixI9nZ2cTHx9d73KVLl1i4cCFlZWWcP3+eVatW0a1bN3bv3k1CQoJrv/j4eA4fPozNZiM0NLTW69hsNmw2m2u5pKQEALvdjt1u91Qzfaa6DWqL+Xi1Pfn5xF3Oqnez23xLDVYMel3Own7uHERHN+lLBtL7E0htgdZph+nCJTc3l+irvomjoqLIy8trMFwyMjJYvXo1YWFhbN++nSVLlrBmzZo6Xy8yMpKCggJ69uxZ63WWL1/OsmXLaq1PS0sjIiKima0yn9TUVF+X4DGB1BbwTnvCz51jQgPbP636WN+lk2lbt1IeG9usrx1I70+gtKWsrMzrX8N04VJRUYFxVRfc4XBcc0J/3bp1rs8nTZrEE0880azXW7p0KQsXLnQtl5SU0Lt3b8aNG1crpPyR3W4nNTWV8ePHExIS4utyWiSQ2gJebk9+PsydW+emAiCz6vMf1nP4uGnTmtVzCZT3J5DaAlBQUOD1r2G6cImNjaWwsNBtXXFxMd27d2/wuPz8fLp27QqA1Wp1BUpsbCzHjh1z27e8vJwuXbrU+TqhoaF1DpeFhIQExDdVtUBqTyC1BbzUnu7dYeBA53UtV/2x9feqj9cDtfomFgsMGEBIbKzz82YIpPcnUNrSGm0w3YT+mDFjOHDggCscLl26hMPhoEePHvUec/DgQRITE13LhmEQHh4OOE9J3rdvn2vb4cOHGT16tHeKFzEriwUef7zOTZ9WfRxX37Hz5zc7WKTtMl24REVFcfvtt7uuWdmxYwezZ8/GMAxmzpxJenp6rWNiYmK48cYbXctpaWlMmTIFgBEjRhAREcHRo84HIW3dupVHH320FVoiYjJz5kBEhPP6lRqqrw6rFS5Wq3P/2bNbozoJMKYbFgN45ZVXWLx4Mbt27SIvL48XX3yRiooK9uzZQ05OjtvZX+AMl7lz55KSkkJwcDDHjx8nOTnZtX39+vUkJyfTt29fbDYbixYtauUWiZhA586wYQPG5Mk4sBKEg/PA11Wbb6uxayVWrBYLlo0bdQGlNIspw6Vz586sXr261vrsGvdBulpSUhJJSUl1buvTpw9r1qzxWH0ifmviRDKff58hS35MODbXkNgIoCtgWCwYBtis4Tje3UiHCQ2dYyZSP9MNi4mId718eCLbcQ4bf9quHfDdkJhlwACsK18iLP80HaYrWKT5TNlzERHvKCqCjW9d5iWcN6RM69kTjh9n3B//6LyPWJcuYLHor05pMYWLSBuybh382+WPiKSU3B49OHT8OBaLhR/85CfOYBHxEP2BItJGGAa8+irMYAMA/6g6JX/UqFH1Xvcl0lzquYi0BYbB51sLsB8q5C42AfC3qpu9jhtX7xUuIs2mnotIICsu5tgTK8kJH8T374rhCEPoxEUMq5Wdnzmf4DJ+/HgfFymBSD0XkUC1cyfMmEHfS7VvUvitw8HJoiJCgdsqKlq/Ngl46rmIBKKqxxkb5eVYMbDifj+x6ocX/xsQMWOGHmcsHqdwEQk0NR5nbKnnIXsfVn28A5wz/TNmOI8T8RCFi0iAOH0aMjLg1H/V/zhjgMt8dyfkieDcr6wM/qLHGYvnKFxEAsTMmZCQYGD7bf2PMwb4J1AO9AKG1dyQoscZi+coXEQCxCOPQK/QAuLJqjXHUlP17MoEwHUjfcOArCy46llKIs2lcBEJELNnQ9q20mvuVx0uE+vaePGiJ0uSNkzhIhJABt3QocHtp3HeYt8C/KiuHSIjPV+UtEkKF5FAEh0NAwfioO4nR35U9fEmILrmBovF+Rhk3QZGPEThIhJIqh5nXN9DiatPQa5zSEyPMxYPUriIBJo5cyi3RFB51Y+3ne/mW+6suUGPMxYvULiIBJjLYZ35iWUDBha3gPkncAHoBiRWr7Ranb0VPc5YPEzhIhJgysvhzIiJrJzwPtb24c7wsFjYWrV9MmDBgmGxQHg4fPAB6HHG4mEKF5EAExXlvFJ/0c6JWHJy4KWXMPr3d4XLNMAycACWl15yXtavYBEv0F2RRQKQtfrPxs6dYf58Dt5+O8dGjCC0XTvGHzmCpXdvTd6LVylcRNqArdu2AZD0ox/Rvk8fH1cjbYGGxUTagK1bnYNi06ZN83El0lYoXEQC3NmzZ9mzZw8AU6ZM8XE10lYoXEQC3KZNmzAMg8TERHr16uXrcqSNULiIBLi//vWvAPzkJz/xcSXSlihcRALY+fPn+fTTTwGYMWOGb4uRNkXhIhLANm/ejMPhYMyYMQwYMMDX5UgbonARCWAaEhNfUbiIBKjCwkI++eQTQENi0voULiIBauvWrVy5coWRI0cyePBgX5cjbYzCRSRAvf3224B6LeIbCheRAHTu3DlSU1MBuO+++3xcjbRFCheRAPTOO+/gcDhITEwkPj7e1+VIG6RwEQlA69evB+CBBx7wcSXSVilcRALM4cOH2bt3L0FBQdxzzz2+LkfaKIWLSIB58803ARg/fjzdunXzcTXSVilcRAKIYRiucNGQmPiSwkUkgHzxxRdkZWURERHBj3/8Y1+XI22YwkUkgKxZswaAu+++mw4dOvi4GmnLFC4iAaK0tJS33noLgEceecTH1Uhbp3ARCRDvvvsupaWlxMfHc9ttt/m6HGnjFC4iAWL16tWAs9disVh8XI20dQoXkQDw9ddfs3v3boKDg5kzZ46vyxFRuIgEgtdffx2AqVOn0r17dx9XI6JwEfF7ly9fZt26dYAm8sU8gn1dQF2KiopYsGAB/fr1o7CwkBUrVhAWFtbgMZ988glff/01AF9++SVPPfUU/fr1A2DWrFmkpaW59k1MTGTjxo1eq1+kNW3cuJHCwkLi4uKYOHGir8sRAUwaLosWLeLee+/ljjvuYNOmTTz33HM8//zz9e5/4cIFXnvtNdfzK/bt28eiRYvYsGEDALfccovrLzuRQGIYBi+99BIAjz76KEFBQb4tSKSK6YbFSktL+fjjj5kwYQIAd9xxB2vXrsXhcNR7THZ2Nv/6179cy8OHD+fgwYOu5eBgU2aoSIvt3r2bvXv30q5dO+bNm+frckRcTPdbNzMzkyFDhmC1OnMvPDycjh07kp2dXe9zKUaOHOm6nxLAoUOHGD58uGv50qVLLFy4kLKyMs6fP8+qVat0Qz8JCNW9lvvvv1/f02IqpguX3NxcoqOj3dZFRUWRl5dXb7gEBQVx6623upZTUlJ4+umnXcsZGRmsXr2asLAwtm/fzpIlS1y3ybiazWbDZrO5lktKSgCw2+3Y7fZmt8ssqtugtphPU9tz6tQp19Dvf/zHf5ju/yGQ3p9Aagu0TjtMFy4VFRUYhuG2zuFwXHNCv9r69euZMWMGI0eOdK2rOd8yadIknnjiiXqPX758OcuWLau1Pi0tjYiIiEbV4A+qH4EbCAKpLdD49vzlL3+hsrKSESNGcPr0aU6fPu3lyponkN6fQGlLWVmZ17+G6cIlNjaWwsJCt3XFxcWNOnf/ww8/pFOnTtx5551u6/Pz8+natSsAVqu1VnjVtHTpUhYuXOhaLikpoXfv3owbN65Wj8of2e12UlNTGT9+PCEhIb4up0UCqS3QtPZcunSJhx56CIBnnnmGSZMmtUaJTRJI708gtQWgoKDA61/DdOEyZswYDhw4gGEYWCwWLl26hMPhoEePHg0el56ezrlz51xXJx84cIBhw4Zx8OBBpkyZQnZ2NuA8uyY8PLze1wkNDSU0NLTW+pCQkID4pqoWSO0JpLZA49rz9ttvU1RUxIABA7jrrrtMfZZYIL0/gdKW1miD6c4Wi4qK4vbbb3ddl7Jjxw5mz56NYRjMnDmT9PT0WscUFxezY8cOV7AYhuEaCouJieHGG2907ZuWlsaUKVNaoSUi3nHlyhV+97vfAfD444+bOlik7TJdzwXglVdeYfHixezatYu8vDxefPFFKioq2LNnDzk5OSQkJLjt/+abb5KSksKrr74KQHl5OWPGjAGc4TJ37lxSUlIIDg7m+PHjJCcnt3aTRDzm7bff5siRI0RHR+uKfDEtU4ZL586dXXd4ral6aOtqjz32GI899li9r5eUlERSUpLH6hPxlcrKSv7rv/4LcF5srAeCiVmZblhMROr33nvv8e233xIVFdXgH1QivqZwEfETDoeD5557DoAFCxbQsWNHH1ckUj+Fi4if2LBhAwcPHqRTp048/vjjvi5HpEEKFxE/ULPX8n/+z/+hc+fOvi1I5BoULiJ+YPPmzXz11VdERkY2eIcJEbNQuIiY3JUrV/jlL38JwPz584mKivJxRSLXpnARMbm1a9fyzTffEB0dzeLFi31djkijKFxETOzSpUv86le/AuDpp5+mU6dOPq5IpHEULiIm9uKLL3L27FkGDBjAz372M1+XI9JoChcRkzpx4gQvvPAC4HwURLt27XxckUjjKVxETOrnP/855eXl3Hbbbfz7v/+7r8sRaRKFi4gJ/e1vf+Ovf/0rVquVlJQULBaLr0sSaRKFi4jJ2Gw21xX4jz32mNtTVUX8hcJFxGReeOEFDh06RLdu3ep85LaIP1C4iJjIyZMnXZP4L7/8si6YFL+lcBExicrKSl555RXsdjvTpk3TJL74NYWLiEmsXLmSw4cPExkZyR/+8AdN4otfU7iImMD+/ft5+umnAVixYgVxcXE+rkikZRQuIj5WXl7Offfdh91uJzExkYcfftjXJYm0mMJFxMf+7//9vxw6dIju3bvz2GOPaThMAkKwrwsQacs2bNjAyy+/DMDq1auprKz0cUUinqGei4iPHDp0iAcffBCAhQsXMnHiRN8WJOJBChcRH7h48SLTp0+ntLSUH/7wh65rW0QChcJFpJU5HA4efPBBvvnmG3r16sU777xDcLBGqCWwKFxEWtnSpUvZuHEj7dq1Y8OGDXTr1s3XJYl4nMJFpBWlpKSwYsUKwDmBP3bsWB9XJOIdCheRVrJhwwaeeOIJAH79618ze/Zs3xYk4kUKF5FW8Omnn3L//fdjGAY/+9nPWLJkia9LEvEqhYuIl/3tb39j0qRJ2Gw27rrrLl5++WVdKCkBT+Ei4kU7d+5kypQplJeXc8cdd/DWW28RFBTk67JEvE7hIuIl27dvZ9q0aVy+fJmpU6eyefNmwsPDfV2WSKtQuIh4wVtvvcX06dOpqKhg+vTp/PWvfyU0NNTXZYm0GoWLiAc5HA6efPJJ7r//fux2O/fccw9vv/027dq183VpIq1KlwWLeEhJSQkPPPAA27ZtA5x3O37++ec1xyJtksJFxAOysrKYNm0aBw8eJDQ0lD/96U/cf//9vi5LxGc0LCbSAoZh8NZbb3HTTTdx8OBBevTowT//+U8Fi7R56rmINFNubi7z5s1j69atACQmJrJp0yZ69uzp48pEfE89F5EmMgyDdevWcf3117N161ZCQkJ47rnn+OyzzxQsIlXUcxFpgtOnTzN37lzef/99AMaMGcOf//xnRowY4ePKRMxFPReRRjh37hw///nPGTx4MO+//z7t2rXj+eefZ/fu3QoWkTqo5yLSgLNnz7JixQpeffVVysvLAbj55pt5/fXXGTZsmI+rEzEvhYtIHc6cOcMLL7zAa6+9xuXLlwEYO3YszzzzDHfccYduPClyDQoXkRqOHDlCSkoKq1evxmazAXDLLbfwzDPPMGHCBIWKSCMpXKRNMwyDjIwMNm/ezKZNmzhw4IBr26233sozzzxDUlKSQkWkiRQu0uZcuXKFf/zjH2zevJnNmzdz6tQp17bg4GCSkpL4xS9+wbhx4xQqIs2kcJE2oaysjI8++ohNmzaxfft2CgsLXdsiIiK48847ufvuu5k0aRJRUVE+rFQkMChcJGCVlJSwadMmNm3axEcffeQ62wuga9euTJs2jbvuuosf/ehHes6KiIeZMlyKiopYsGAB/fr1o7CwkBUrVhAWFtbgMSdOnOCpp56if//+XLlyheeff941pNHQNglceXl5PPjgg67lfv36cffdd3PXXXfx/e9/X3crFvEiU15EuWjRIu69916Sk5MZN24czz333DWPefjhh3nyySd59tlniYmJ4fXXX2/UNglc8fHx/Pu//zvPPPMM+/fvJzs7m9/97nf84Ac/ULCIeJnpwqW0tJSPP/6YCRMmAHDHHXewdu1aHA5HvcdkZWVRVFTE0KFDAZg6dSqrVq265jYJfO+++y7JycmMGjVKvVWRVmS6YbHMzEyGDBmC1erMvfDwcDp27Eh2djbx8fF1HrN7924SEhJcy/Hx8Rw+fBibzdbgtroeO2uz2VzXNwBcuHABwG0C2J/Z7XbKysooKCggJCTE1+W0SCC1BdQeMwuktsB3v88Mw/Da1zBduOTm5hIdHe22Lioqiry8vHrDpa5jIiMjKSgoaHBbXXewXb58OcuWLau1fvDgwU1tioiIqRUUFNCpUyevvLbpwqWioqJWmjocjgYn9Bs6pqmvt3TpUhYuXOhaLi4upm/fvpw8edJrb0JrKikpoXfv3pw6dYqOHTv6upwWCaS2gNpjZoHUFnCOyPTp04cuXbp47WuYLlxiY2NrDUEVFxfTvXv3Bo85duyY27ry8nK6dOnS4La6hIaG1jlc1qlTp4D4pqrWsWPHgGlPILUF1B4zC6S2AK7pB6+8ttdeuZnGjBnDgQMHXL2NS5cu4XA46NGjR73HJCYmsm/fPtfy4cOHGT169DW3iYiId5guXKKiorj99ttJS0sDYMeOHcyePRvDMJg5cybp6em1jhkxYgQREREcPXoUgK1bt/Loo49ec5uIiHiH6YbFAF555RUWL17Mrl27yMvL48UXX6SiooI9e/aQk5PjdvZXtfXr15OcnEzfvn2x2WwsWrSoUduuJTQ0lGeeeabOoTJ/FEjtCaS2gNpjZoHUFmid9lgMb56LJiIibZLphsVERMT/KVxERMTjFC4iIuJxChcREfE4U54t5m2Bdkv/5rTnk08+4euvvwbgyy+/5KmnnqJfv34AzJo1y3UqODivFdq4caPX6q+pOW1pqF5/fG969eqFzWZz7We32zl58iShoaE+fW+ys7P56KOPWL58OSdOnGjUMWb+uWlOe8z6cwPNa49Xf3aMNuihhx4yduzYYRiGYWzcuNF48sknr3nM7bffbhw8eNAwDMP43e9+Z7z22muN2tYamtqe4uJi45577nEtZ2RkGNOnT3ct/+EPf/BOoY3QnPemoXr97b0pKSkxFixY4FouKioyVq5c6Vr25XtTrXfv3o3e18w/N9Ua2x4z/9zU1JT3x5s/O20uXC5evGj07t3bqKysNAzDMMrKyowePXq4luty9OhR44YbbnAtHzlyxLXc0LbW0Jz2ZGRkGH369HEtV1RUGNddd51r+Y9//KP3Cm5Ac9piGPXX64/vzfnz540zZ864ln/7298aJSUlrmVfvTc19e3bt1H7mfnnpqbGtsesPzdXa2x7DMO7PzttbljM17f097TmtGfkyJG8+eabruVDhw4xfPhw1/KlS5dYuHAhZWVlnD9/nlWrVtGtWzfvNoTmtaWhev3xvenatavr84sXL1JSUkJkZKRrna/em+Yw889Nc5j156YlvPmz0+Ym9Bu6pX9TjmnMLf1bQ3PaExQUxK233upaTklJ4emnn3YtZ2Rk8Otf/5pXX32Vhx56iCVLlni+8Do0py1Qf73++N7UtHr1au6++263db56b5rDzD83zWHWn5uW8ObPTpsLF1/f0t/TWvr1169fz4wZMxg5cqRr3bp161zHT5o0iX/84x+eK7gBzW1LffX6+3uzbds2Ro0a5bbOV+9Nc5j556alzPRz0xLe/Nlpc+HS3Fv6X31MzVv617etNTSnPdU+/PBDOnXqxJ133um2Pj8/3/W51Wr16tPqampuW+qr15/fm3379tX5/CBfvTfNYeafm5Yw289NS3jzZ6fNhUug3dK/Oe0BSE9P59y5c0ydOhWAAwcOAHDw4EESExNd+xmGQXh4uJeqd9ectjRUr7++NwCfffYZsbGxbut8+d40h5l/bprLjD83zeXtn502Fy6Bdkv/5rSnuLiYHTt2MGfOHMD5TbVu3ToAYmJiuPHGG137pqWlMWXKlFZoSfPa0lC9/vjeVDty5AgVFRVu63z53tRkt9tdgelwOPzy56amxrbHrD83V2tse7z9s9Mm74pcXFzM4sWL6dOnj+uW/oZhcP311/P73/+eH//4x7WOOXnypNtt+2teUNTQNjO25w9/+APLli2jXbt2gLO7O2bMGFJTUwH429/+xoEDBwgODub48eMkJycTERFhyrZcq15/e2+qJScnU15ezgsvvOC23pfvTVZWFu+99x5Lly5lwYIF3H///QwbNsxvf26a2h4z/9w0pz3Xqrml70+bDBcREfGuNjcsJiIi3qdwERERj1O4iIiIxylcRETE4xQuIiLicQoXERHxOIWLiIh4nMJFREQ8TuEi0oDz589jsVgYNmxYvfscPHgQi8XCD37wg1rbdu/ejcVi4U9/+pNHXk/EXyhcRBpQfU+mmg9Outr//M//1LvPli1bsFqtrns2tfT1RPyFwkWkARkZGQBuN/i7WkOBsWXLFm6++WbXHY5b+noi/kLhItKAlvQ0jh49yqFDh9xuGKiei7QVunGlSAP69+/PqVOnKCkpqfMOt5WVlXTs2BGLxUJJSQlW63d/r7344ossXryYb775hiFDhrT49UT8ib5zRepRVFTE8ePHue666+q9dfqhQ4coKytj9OjRtYJgy5YtDB482BUsLX09EX+i716RelQPYTU0P1LfEFZ+fj6ff/45d911l0deD2DevHk8+OCDjaq9PvPmzeOBBx5o0WuINIbCRaQeLZl837ZtGw6Hw22+paWT+V9++SUjR45sZPV1+/LLLxk1alSLXkOkMRQuIvWofob4DTfcUO8+X3zxBVA7MLZs2UK3bt24+eabPfJ6hmHw1VdftSgYql+jpQEl0hgKF5F6HDhwAIABAwbUuf38+fOkp6fTvXt3rr/+etf68vJyUlNTmTp1qtu8SXNfD5yPsC0tLaW8vJxbb72ViIgIEhISyM7OBmDz5s107drV7Zjt27e7rcvOzqa0tJTS0lISExNp3749SUlJ5OTkNPa/RKTRFC4i9bDb7YDzl35dXnnlFSorK2vNYaSmplJWVlbrmeXNfT2AzMxMgoKCeOWVV1ixYgV79+7lypUrrFixAoD9+/fX6tXs37/frZeSmZmJ1WolJSWFP/zhD3z22Wfk5eUxf/78hv4bRJpF4SJSj5tuugmAF154gcrKStd6wzBYu3Yty5cvJzo6ml/84hdux23ZsoWIiAh+9KMfeeT1wBkMsbGxvPvuu3zve99j2LBh/OhHPyI/P9+1/epwyczMdAuXL7/8kg4dOrB582ZuuukmbrjhBhYvXsw//vGP5vz3iDQo2NcFiJjVM888w44dO3jrrbfYs2cPt9xyC3a7nczMTL755hu6dOnCtm3biImJcR3jcDjYvn07EyZMIDw8vMWvVy0zM5N77rmHjh07utYdP36c+Ph4wNlLubqntH//fu68806315g1axZRUVGude3bt8fhcLTsP0qkDuq5iNRj4MCBpKen88gjj1BRUcHbb7/N9u3bCQ0NZcmSJRw8eJBbbrnF7Zh//etf5OXl1fpF39zXq5aZmcnYsWPd1lUPhZWUlHD8+HG3nktRURFZWVm1ei5X92727duns8fEK9RzEWlAnz59WL16daP3v/pGlS19PYALFy5w4sQJtxAoKSnh2LFjjBw50jWpP2jQINf2d955x+3uyxcvXuTYsWNuw3Hl5eW8/vrr/OY3v2lSPSKNoZ6LiAdt2bKF733ve7XO3GqJzMxMwsPD3cIjMzOTdu3acd111xEdHQ18dwFmamoqv/rVrxg0aJBraO6rr74iNDSUVatWsX//fr7++mvuvPNOhgwZwqxZszxWq0g19VxEPOjbb7/1+GtmZmYybNgwgoKC3NYNHTqU4OBgevfuTXJyMnfffTedO3fmzjvvZNq0aZSUlNR6jccff5w77riD8vJy7rvvPn7729+6va6Ip+jGlSIi4nEaFhMREY9TuIiIiMcpXERExOMULiIi4nEKFxER8TiFi4iIeJzCRUREPE7hIiIiHqdwERERj1O4iIiIxylcRETE4xQuIiLicf8fxLLeubKNc8MAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 6))\n",
    "plt.plot(S1/S1[4],X1/X1[4],marker = '*',markersize = 10,color = 'b')\n",
    "plt.plot(S2/S2[4],X1/X1[4],marker = 'o',markersize = 10,color = 'r')\n",
    "h = 0.6\n",
    "α = 0.15\n",
    "H = np.arange(0.01,1.5,0.01)\n",
    "v =8\n",
    "V = v*(H/h)**α\n",
    "plt.plot(V/8,H/0.6,color = 'black')\n",
    "plt.grid()\n",
    "plt.xlabel('${U/U_{hub}}$',fontsize= 14)\n",
    "plt.ylabel('${Z/H}$',fontsize= 14)\n",
    "plt.xlim(0,1.5)\n",
    "plt.ylim(0,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "60212143-7065-408b-adb2-2bc0518c9f09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# 计算理论廓线（只计算一次）\n",
    "h, α, v = 0.6, 0.15, 8\n",
    "H = np.arange(0.01, 1.5, 0.01)\n",
    "V = v * (H/h)**α\n",
    "\n",
    "# 绘图函数\n",
    "def plot_wind_profile(x_data, y_data, marker, color, title):\n",
    "    plt.figure(figsize=(4, 6))  # 保持原始尺寸\n",
    "    plt.plot(x_data/x_data[4], y_data/y_data[4], marker=marker, markersize=10, color=color)\n",
    "    plt.plot(V/8, H/0.6, color='black')  # 绘制理论曲线\n",
    "    plt.grid()\n",
    "    plt.xlabel('${U/U_{hub}}$', fontsize=14)\n",
    "    plt.ylabel('${Z/H}$', fontsize=14)\n",
    "    plt.xlim(0, 1.5)\n",
    "    plt.ylim(0, 2)\n",
    "    plt.title(title, fontsize=12)  # 添加标题\n",
    "    plt.tight_layout()  # 确保布局完整\n",
    "\n",
    "# 分别绘制两张图\n",
    "plot_wind_profile(S1, X1, '*', 'b', '第一组数据拟合曲线')\n",
    "plot_wind_profile(S2, X1, 'o', 'r', '第二组数据拟合曲线')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e27633b-77a6-498b-ad7d-c85ffd5b30d4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
